Dataset Information

Prediction of clinical outcome and survival in soft-tissue sarcoma using a ten-lncRNA signature.

ABSTRACT: The prognostic value of long non-coding RNAs (lncRNAs) in patients with soft-tissue sarcoma has rarely been unraveled. The aim of the study was to find a lncRNA signature to predict the clinical outcome and survival in soft-tissue sarcoma based on the high-throughput RNA-seq data from The Cancer Genome Atlas (TCGA) database. The lncRNAs which closely correlated with overall survival in 258 soft-tissue sarcoma patients were identified with Cox proportional regression model. Ten lncRNAs, including RP11-560J1.2, AP001432.14, RP4-665J23.1, LINC00680, AC006129.2, RP11-230G5.2, BACH1-IT2, RP11-274B21.9, RP11-504A18.1 and RP11-713P17.3, were selected to calculate a risk score. The risk score could effectively predict patients' outcome, such as the status of mitotic count of tumor cells, person neoplasm cancer and residual tumor. More inspiringly, the risk score generated from the 10-lncRNA signature was an independent prognostic indicator for soft-tissue sarcoma patients. Overall, this 10-lncRNA signature gains the potential as an effective prognostic tool for soft-tissue sarcoma as part of the integrated clinical RNA-seq program.

SUBMITTER: He RQ

PROVIDER: S-EPMC5655202 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Prediction of clinical outcome and survival in soft-tissue sarcoma using a ten-lncRNA signature.

He Rong-Quan RQ Wei Qing-Jun QJ Tang Rui-Xue RX Chen Wen-Jie WJ Yang Xia X Peng Zhi-Gang ZG Hu Xiao-Hua XH Ma Jie J Chen Gang G

Oncotarget 20170522 46

The prognostic value of long non-coding RNAs (lncRNAs) in patients with soft-tissue sarcoma has rarely been unraveled. The aim of the study was to find a lncRNA signature to predict the clinical outcome and survival in soft-tissue sarcoma based on the high-throughput RNA-seq data from The Cancer Genome Atlas (TCGA) database. The lncRNAs which closely correlated with overall survival in 258 soft-tissue sarcoma patients were identified with Cox proportional regression model. Ten lncRNAs, including ...[more]

PMID: 29113307

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Project description:BackgroundVarious fusion strategies (feature-level fusion, matrix-level fusion, and image-level fusion) were used to fuse PET and MR images, which might lead to different feature values and classification performance. The purpose of this study was to measure the classification capability of features extracted using various PET/MR fusion methods in a dataset of soft-tissue sarcoma (STS).MethodsThe retrospective dataset included 51 patients with histologically proven STS. All patients had pre-treatment PET and MR images. The image-level fusion was conducted using discrete wavelet transformation (DWT). During the DWT process, the MR weight was set as 0.1, 0.2, 0.3, 0.4, …, 0.9. And the corresponding PET weight was set as 1- (MR weight). The fused PET/MR images was generated using the inverse DWT. The matrix-level fusion was conducted by fusing the feature calculation matrix during the feature extracting process. The feature-level fusion was conducted by concatenating and averaging the features. We measured the predictive performance of features using univariate analysis and multivariable analysis. The univariate analysis included the Mann-Whitney U test and receiver operating characteristic (ROC) analysis. The multivariable analysis was used to develop the signatures by jointing the maximum relevance minimum redundancy method and multivariable logistic regression. The area under the ROC curve (AUC) value was calculated to evaluate the classification performance.ResultsBy using the univariate analysis, the features extracted using image-level fusion method showed the optimal classification performance. For the multivariable analysis, the signatures developed using the image-level fusion-based features showed the best performance. For the T1/PET image-level fusion, the signature developed using the MR weight of 0.1 showed the optimal performance (0.9524(95% confidence interval (CI), 0.8413-0.9999)). For the T2/PET image-level fusion, the signature developed using the MR weight of 0.3 showed the optimal performance (0.9048(95%CI, 0.7356-0.9999)).ConclusionsFor the fusion of PET/MR images in patients with STS, the signatures developed using the image-level fusion-based features showed the optimal classification performance than the signatures developed using the feature-level fusion and matrix-level fusion-based features, as well as the single modality features. The image-level fusion method was more recommended to fuse PET/MR images in future radiomics studies.

Project description:Endometrial carcinoma (EC) is one of the common gynecological cancers with increasing incidence and revived mortality recently. Given the heterogeneity of tumors and the complexity of lncRNAs, a panel of lncRNA biomarkers might be more precise and stable for prognosis. In the present study, we developed a new lncRNA model to predict the prognosis of patients with EC. EC-associated differentially expressed long noncoding RNAs (lncRNAs) were identified from The Cancer Genome Atlas (TCGA). Univariate COX regression and least absolute shrinkage and selection operator (LASSO) model were selected to find the 8-independent prognostic lncRNAs of EC patient. Furthermore, the risk score of the 3-lncRNA signature for overall survival (OS) was identified as CTD-2377D24.6 expression × 0.206 + RP4-616B8.5 × 0.341 + RP11-389G6.3 × 0.343 by multivariate Cox regression analysis. According to the median cutoff value of this prognostic signature, the EC samples were divided into two groups, high-risk set (3-lncRNAs at high levels) and low-risk set (3-lncRNAs at low levels), and the Kaplan-Meier survival curves demonstrated that the low-risk set had a higher survival rate than the high-risk set. In addition, the 3-lncRNA signature was closely linked with histological subtype (p = 0.0001), advanced clinical stage (p = 0.011), and clinical grade (p < 0.0001) in EC patients. Our clinical samples also confirmed that RP4-616B8.5, RP11-389G6.3, and CTD-2377D24.6 levels were increased in tumor tissues by qRT-PCR and in situ hybridization. Intriguingly, the p-value of combined 3-lncRNAs was lower than that of each lncRNA, indicating that the 3-lncRNA signature also showed higher performance in EC tissue than paracancerous. Functional analysis revealed that cortactin might be involved in the mechanism of 3-lncRNA signatures. These findings provide the first hint that a panel of lncRNAs may play a critical role in the initiation and metastasis of EC, indicating a new signature for early diagnosis and therapeutic strategy of uterine corpus endometrial carcinoma.

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Dataset Information

Prediction of clinical outcome and survival in soft-tissue sarcoma using a ten-lncRNA signature.

Publications

Prediction of clinical outcome and survival in soft-tissue sarcoma using a ten-lncRNA signature.

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